Job Description :

Skilled Data Engineer to lead and support the migration of enterprise data integration workflows from SnapLogic to AWS Glue. The ideal candidate has strong experience in cloud-based ETL/ELT design, AWS data services, and modern data engineering best practices. You will play a key role in re-architecting pipelines to be scalable, cost-efficient, and aligned with AWS-native patterns.

Job Responsibilities :

- Analyze existing SnapLogic pipelines, snaps, and workflows and translate them into AWS Glue jobs (PySpark).

- Design, develop, and optimize ETL/ELT pipelines using AWS Glue, S3, IAM, CloudWatch, and related services.

- Refactor integration logic to follow AWS best practices for performance, reliability, and cost optimization.

- Collaborate with data architects, analytics teams, and stakeholders to validate data mappings and business logic.

- Implement data ingestion from various sources (databases, APIs, files, SaaS platforms).

- Ensure data quality, validation, error handling, logging, and monitoring.

- Migrate scheduling, orchestration, and dependencies (e.g., SnapLogic tasks - Glue Triggers / Step Functions).

- Support testing, UAT, and production rollout of migrated pipelines.

- Document architecture, pipelines, and operational procedures.

- Troubleshoot production issues and optimize Glue job performance

Requirements :

- Hands-on experience with SnapLogic (pipeline design, snaps, error handling)

- Strong experience with AWS Glue (PySpark)

- Proficiency in Python and SQL

- Experience with AWS S3, IAM, CloudWatch

- Understanding of data warehousing concepts and data modeling

- Experience migrating or modernizing ETL platforms

- Familiarity with CI/CD for data pipelines

- Strong problem-solving and communication skills